1. Field of the Disclosure
Embodiments disclosed herein are related generally to the field of well drilling. More specifically, embodiments disclosed herein relate to methods of optimizing drilling tool assemblies for use in well drilling operations. More specifically still, embodiments disclosed herein relate to methods of optimizing drilling tool assembles using artificial neural networks.
2. Background Art
FIG. 1 shows one example of a conventional drilling system for drilling an earth formation. The drilling system includes a drilling rig 10 used to turn a drilling tool assembly 12 which extends downward into a wellbore 14. Drilling tool assembly 12 includes a drilling string 16, a bottom hole assembly (“BHA”) 18, and a drill bit 20, attached to the distal end of drill string 16.
Drill string 16 comprises several joints of drill pipe 16a connected end to end through tool joints 16b. Drill string 16 transmits drilling fluid (through its central bore) and transmits rotational power from drill rig 10 to BHA 18. In some cases drill string 16 further includes additional components such as subs, pup joints, etc. Drill pipe 16a provides a hydraulic passage through which drilling fluid is pumped. The drilling fluid discharges through selected-size orifices in the bit (“jets”) for the purposes of cooling the drill bit and lifting rock cuttings out of the wellbore as it is being drilled.
Bottom hole assembly 18 includes a drill bit 20. Typical BHAs may also include additional components attached between drill string 16 and drill bit 20. Examples of additional BHA components include drill collars, stabilizers, measurement-while-drilling (“MWD”) tools, logging-while-drilling (“LWD”) tools, and downhole motors.
In general, drilling tool assemblies 12 may include other drilling components and accessories, such as special valves, kelly cocks, blowout preventers, and safety valves. Additional components included in drilling tool assemblies 12 may be considered a part of drill string 16 or a part of BHA 18 depending on their locations in drilling tool assembly 12.
Drill bit 20 in BHA 18 may be any type of drill bit suitable for drilling earth formation. The most common types of earth boring bits used for drilling earth formations are fixed-cutter (or fixed-head) bits, roller cone bits, and percussion bits. FIG. 2 shows one example of a fixed-cutter bit. FIG. 3 shows one example of a roller cone bit.
Referring now to FIG. 2, fixed-cutter bits (also called drag bits) 21 typically comprise a bit body 22 having a threaded connection at one end 24 and a cutting head 26 formed at the other end. Cutting head 26 of fixed-cutter bit 21 typically comprises a plurality of ribs or blades 28 arranged about a rotational axis of the bit and extending radially outward from bit body 22. Cutting elements 29 are preferably embedded in the blades 28 to engage formation as bit 21 is rotated on a bottom surface of a wellbore. Cutting elements 29 of fixed-cutter bits may comprise polycrystalline diamond compacts (“PDC”), specially manufactured diamond cutters, or any other cutter elements known to those of ordinary skill in the art. These bits 21 are generally referred to as PDC bits.
Referring now to FIG. 3, a roller cone bit 30 typically comprises a bit body 32 having a threaded connection at one end 34 and one or more legs 31 extending from the other end. A roller cone 36 is mounted on a journal (not shown) on each leg 31 and is able to rotate with respect to bit body 32. On each cone 36, a plurality of cutting elements 38 are shown arranged in rows upon the surface of cone 36 to contact and cut a formation encountered by bit 30. Roller cone bit 30 is designed such that as it rotates, cones 36 of bit 30 roll on the bottom surface of the wellbore and cutting elements 38 engage the formation therebelow. In some cases, cutting elements 38 comprise milled steel teeth and in other cases, cutting elements 38 comprise hard metal inserts embedded in the cones. Typically, these inserts are tungsten carbide inserts or polycrystalline diamond compacts, but in some cases, hardfacing is applied to the surface of the cutting elements to improve wear resistance of the cutting structure.
Referring again to FIG. 1, for drill bit 20 to drill through formation, sufficient rotational moment and axial force must be applied to bit 20 to cause the cutting elements to cut into and/or crush formation as bit 20 is rotated. Axial force applied to bit 20 is typically referred to as the weight on bit (“WOB”). Rotational moment applied to drilling tool assembly 12 by drill rig 10 (usually by a rotary table or a top drive) to turn drilling tool assembly 12 is referred to as the rotary torque. The speed at which drilling rig 10 rotates drilling tool assembly 12, typically measured in revolutions per minute (“RPM”), is referred to as the rotary speed. Additionally, the portion of the weight of drilling tool assembly 12 supported by a suspending mechanism of rig 10 is typically referred to as the hook load.
The speed and economy with which a wellbore is drilled, as well as the quality of the hole drilled, depend on a number of factors. These factors include, among others, the mechanical properties of the rocks which are drilled, the diameter and type of the drill bit used, the flow rate of the drilling fluid, and the rotary speed and axial force applied to the drill bit. It is generally the case that for any particular mechanical property of a formation, a drill bit's rate of penetration (“ROP”) corresponds to the amount of axial force on and the rotary speed of the drill bit. The rate at which the drill bit wears out is generally related to the ROP. Various methods have been developed to optimize various drilling parameters to achieve various desirable results.
Prior art methods for optimizing values for drilling parameters that primarily involve looking at the formation have focused on the compressive strength of the rock being drilled. For example, U.S. Pat. No. 6,346,595, issued to Civolani, el al. (“the '595 patent”), and assigned to the assignee of the present invention, discloses a method of selecting a drill bit design parameter based on the compressive strength of the formation. The compressive strength of the formation may be directly measured by an indentation test performed on drill cuttings in the drilling fluid returns. The method may also be applied to determine the likely optimum drilling parameters such as hydraulic requirements, gauge protection, WOB, and the bit rotation rate. The '595 patent is hereby incorporated by reference in its entirety.
U.S. Pat. No. 6,424,919, issued to Moran, et al. (“the '919 patent”), and assigned to the assignee of the present invention, discloses a method of selecting a drill bit design parameter by inputting at least one property of a formation to be drilled into a trained Artificial Neural Network (“ANN”). The '919 patent also discloses that a trained ANN may be used to determine optimum drilling operating parameters for a selected drill bit design in a formation having particular properties. The ANN may be trained using data obtained from laboratory experimentation or from existing wells that have been drilled near the present well, such as an offset well. The '919 patent is hereby incorporated by reference in its entirety.
ANNs are a relatively new data processing mechanism. ANNs emulate the neuron interconnection architecture of the human brain to mimic the process of human thought. By using empirical pattern recognition, ANNs have been applied in many areas to provide sophisticated data processing solutions to complex and dynamic problems (e.g., classification, diagnosis, decision making, prediction, voice recognition, military target identification).
Similar to the human brain's problem solving process, ANNs use information gained from previous experience and apply that information to new problems and/or situations. The ANN uses a “training experience” (i.e., the data set) to build a system of neural interconnects and weighted links between an input layer (i.e., independent variable), a hidden layer of neural interconnects, and an output layer (i.e., the dependant variables or the results). No existing model or known algorithmic relationship between these variables is required, but such relationships may be used to train the ANN. An initial determination for the output variables in the training exercise is compared with the actual values in a training data set. Differences are back-propagated through the ANN to adjust the weighting of the various neural interconnects, until the differences are reduced to the user's error specification. Due largely to the flexibility of the learning algorithm, non-linear dependencies between the input and output layers, can be “learned” from experience.
Several references disclose various methods for using ANNs to solve various drilling, production, and formation evaluation problems. These references include U.S. Pat. No. 6,044,325 issued to Chakravarthy, et al., U.S. Pat. No. 6,002,985 issued to Stephenson, et al., U.S. Pat. No. 6,021,377 issued to Dubinsky, et al., U.S. Pat. No. 5,730,234 issued to Putot, U.S. Pat. No. 6,012,015 issued to Tubel, and U.S. Pat. No. 5,812,068 issued to Wisler, et al.
However, one skilled in the art will recognize that optimization predictions from these methods may not be as accurate as simulations of drilling, which may be better equipped to make predictions for each unique situation.
Simulation methods have been previously introduced which characterize either the interaction of a bit with the bottom hole surface of a wellbore or the dynamics of BHA.
One simulation method for characterizing interaction between a roller cone bit and an earth formation is described in U.S. Pat. No. 6,516,293 (“the '293 patent”), entitled “Method for Simulating Drilling of Roller Cone Bits and its Application to Roller Cone Bit Design and Performance,” and assigned to the assignee of the present invention. The '293 patent discloses methods for predicting cutting element interaction with earth formations. Furthermore, the '293 patent discloses types of experimental tests that can be performed to obtain cutting element/formation interaction data. The '293 patent is hereby incorporated by reference in its entirety. Another simulation method for characterizing cutting element/formation interaction for a roller cone bit is described in Society of Petroleum Engineers (SPE) Paper No. 29922 by D. Ma et al., entitled, “The Computer Simulation of the Interaction Between Roller Bit and Rock”.
Methods for optimizing tooth orientation on roller cone bits are disclosed in PCT International Publication No. WO00/12859 entitled, “Force-Balanced Roller-Cone Bits, Systems, Drilling Methods, and Design Methods” and PCT International Publication No. WO00/12860 entitled, “Roller-Cone Bits, Systems, Drilling Methods, and Design Methods with Optimization of Tooth Orientation.
Similarly, SPE Paper No. 15618 by T. M. Warren et al., entitled “Drag Bit Performance Modeling” discloses a method for simulating the performance of PDC bits. Also disclosed are methods for defining the bit geometry and methods for modeling forces on cutting elements and cutting element wear during drilling based on experimental test data. Examples of experimental tests that can be performed to obtain cutting element/earth formation interaction data are also disclosed. Experimental methods that can be performed on bits in earth formations to characterize bit/earth formation interaction are discussed in SPE Paper No. 15617 by T. M. Warren et al., entitled “Laboratory Drilling Performance of PDC Bits”.
Present systems for optimizing drilling parameters, as described above, focus on either optimizing drilling components or optimizing drilling conditions. Drilling components may be optimized by tailoring such components for specific well conditions. During such design processes, drill bits, BHAs, drillstrings, and/or drilling tool assemblies may be simulated and adjusted according to the anticipated formation the drilling tool will be drilling. These design processes may involve complex simulations including three dimensional modeling, finite element analysis, and/or graphical representations. Such design processes may require vast amounts of time that, while still in the design and manufacturing stage may be readily available. However, while drilling a wellbore, when downhole conditions change, or when the formation deviates from the anticipated structure, even optimized components may fail or be less efficient than predicted.
During drilling operations, drilling operators may rely on historical data sets, offset well formation data, monitored downhole drilling conditions, and personal experience to anticipate and/or determine when a wellbore condition has changed. A drilling operator may decide to change drilling parameters (e.g., axial load, rotational speed, drilling fluid flow rate, etc.) in response to changing downhole conditions. However, the drilling operator's response may be based on a limited number of options and/or experiences. Alternatively, the drilling operator may research the given conditions, and base a drilling parameter adjustment on such research. However, during drilling, running programs that calculate optimized drilling parameter adjustment are time intensive and may result in substantial rig downtime.
Traditionally, the optimization of drilling components has involved the finite knowledge of a drilling operator when designing and assembling individual drilling components. Examples of such optimization practices may have previously included a drilling operator selecting a drill bit, reamers, spacers, vibration dampeners, and other drilling components based on their individual experience with such devices. The drilling operator, using their own limited experience then assembled such devices according to their experience, and the drilling assembly was used to drill a wellbore. However, more recently, advances in drilling optimization programs have allowed a drilling operators own experience to be supplemented with external experience and historical data. The progression of such optimization programs currently allows a drilling operator to run a simulation of numerous drilling components, as described above, thereby providing for an end product that is further optimized to drill in a specified formation.
Such a computer assisted drilling optimization program may allow an operator to supplement their own knowledge with the knowledge of other drilling assembly designers, experience data from other wells, off-set well data, historical bit runs, or data based on simulated drill runs. Using a computer assisted optimization system, the drilling operator may now input known and/or expected formation variables (e.g., formation type), along with their personal experience data (e.g., a starting point for a drilling assembly, including bit type, or desirable drilling components), and allow the computer optimization system to iteratively determine the optimized components of a drilling assembly. Such systems provide for drill assemblies that may be optimized for a given formation, but are still constrained by the experience data of the human operator. Because the computer simulation necessarily begins with the constrained knowledge of the human drilling operator, the iterative process may initially involve many repetitive, and in certain instances needless operations to remove the constraints of the human operator from limiting the optimized drilling assembly.
For example, when a drilling operator beings a simulation of a drilling assembly, the drilling operator may initially provide the computer optimization program formation variable and when they believe to be an optimized drilling assembly. The simulation program then iteratively simulates the preselected drilling assembly a number of times, making small changes in the design of the drilling assembly to optimize such assembly according to the formation variables provided by the human operator. However, in certain instances, the drill bit initially selected by the drilling operator may be substantially not optimized for the selected formation. As such, the computer optimization system begins its simulation based on an incorrect assumption (i.e., the drilling assembly selected by the drilling operator). Because the human operator has supplied incorrect initial constraints to the system, the computer optimization system may either take much time to arrive at an optimized drilling assembly, thereby wasting valuable resources and time or, in certain instances, never arrive at an optimized assembly.
While current computer assisted optimization systems used in designing drilling assemblies may provide for relatively optimized components, because the methods are based on initial human constraints, the systems are inefficient. Thus, there exists a need for a drilling assembly optimization system to guide the design of a drilling assembly to achieve an optimized drilling assembly using a minimum number of simulations. Furthermore, there exists a continuing need for a drill assembly optimization system to control guide adjustments to the drilling assembly throughout the drilling process.